Optimization of battery charging strategy based on nonlinear model predictive control

Yujie Wang, Caijie Zhou and Zonghai Chen

Energy, 2022, vol. 241, issue C

Abstract: With the increased applications of lithium-ion batteries in energy storage systems and electric vehicles, there is a growing demand for battery energy storage systems and management systems. Considering that the temperature especially internal temperature significantly can affect the performance and safety of the battery, a triple-objective optimization charging method which can reduce the cell charging time, energy loss, and internal temperature rise is proposed based on a thermoelectric coupling model in this paper. Specifically, a thermoelectric coupling model suitable for a wide temperature range from −5 °C to 45 °C is formulated. On this basis, a nonlinear model predictive control framework is proposed to obtain the real-time charging current by solving the nonlinear optimization problems. The impacts of the objective function weights and internal temperature thresholds on the charging result are discussed through experiments, and another multi-stage constant current charging method is conducted as a comparison. Results show that the nonlinear model predictive control can achieve a good balance between three objectives while satisfying constraints. Compared with the traditional multistage constant current charging method, the proposed strategy can reduce energy loss by 150 J and temperature rise by 1–2 °C in similar charging time.

Keywords: Charging optimization; Thermoelectric coupling model; Nonlinear model predictive control; Parameter identification; Lithium-ion batteries (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031261

DOI: 10.1016/j.energy.2021.122877

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